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Please use this identifier to cite or link to this item: http://dspace.univ-ouargla.dz/jspui/handle/123456789/21997

Titre: Denoising Medical Ultrasound Images and Error Estimate by Cellular Neural Networks and Translation Invariant Wavelets
Auteur(s): Rachida, BOUCHOUAREB
Mots-clés: wavelets transform
image processing
ultrasound image
cellular neural network
Issue Date: 18-Nov-2014
Editeur: Université des Frères Mentouri Constantine 1
Résumé: Speckle Noise is a natural characteristic of medical ultrasound images. It is a term used for the granular form that appears in B-Scan and can be considered as a kind of multiplicative noise. Speckle Noise reduces the ability of an observer to distinguish fine details in diagnostic testing. It also limits the effective implementation of image processing such as edge detection, segmentation and volume rendering in 3 D. Therefore; treatment methods of speckle noise were sought to improve the image quality and to increase the capacity of diagnostic medical ultrasound images. Such as median filters, Wiener and linear filters (Persona & Mali, SRAD ...).The first method used in this work is newly invented by Chua & Yang called Cellular Neural Networks (CNN), the second method is 2-D translation invariant forward wavelet transform, both are used in image processing, including noise reduction applications in medical imaging.
Description: The Second International Conference on Electrical Engineering and Control Applications
URI: http://dspace.univ-ouargla.dz/jspui/handle/123456789/21997
Appears in Collections:2. Faculté des nouvelles technologies de l’information et de la communication

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